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Asymmetric and threshold effects of FinTech on poverty in SSA countries

Author

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  • Noha Emara

Abstract

Purpose - The purpose of this paper is to analyze the dynamic asymmetric relationship between financial technology (FinTech) adoption and poverty alleviation on annual data for the Sub-Saharan Africa (SSA) region over the period from 2004 to 2020. Design/methodology/approach - This study adopted the general method of moments (GMM) method on annual data for 127 countries including 45 countries from the SSA region over the period from 2004 to 2020. Findings - The study’s findings show that improvement in FinTech may initially decrease the rate of extreme poverty, leading to a decrease in total poverty as a percent of the population. While there is an initial decrease in the rate of extreme poverty with improvements of FinTech, once the FinTech index reaches its threshold level of 37.18 points, further improvement in FinTech tends to decrease as penetration increases, giving rise to an decrease in the rate of poverty alleviation. Research limitations/implications - Policymakers should design more aggressive and comprehensive policies directed at recouping the maximum gains of FinTech adoption, with a reasonable threshold target. Practical implications - Policymakers in the SSA region must be aware of a FinTech threshold level of 37.18 points. To ensure the highest reduction in extreme poverty, policymakers must keep investing in FinTech to reach this threshold level. Social implications - FinTech improvement leads to poverty alleviation. Policymakers in the SSA region can fully recoup the benefits of FinTech by achieving a pre-set threshold level. Originality/value - This paper addresses that gap in the literature by studying the impact of FinTech, instead of the traditional financial inclusion measures, on poverty in the 45 countries in the SSA region, exploring the potential dynamic asymmetry of this poverty-FinTech link, and testing the presence and statistical significance of the threshold level of FinTech.

Suggested Citation

  • Noha Emara, 2022. "Asymmetric and threshold effects of FinTech on poverty in SSA countries," Journal of Economic Studies, Emerald Group Publishing Limited, vol. 50(5), pages 921-946, August.
    • Handle: RePEc:eme:jespps:jes-03-2022-0158
    DOI: 10.1108/JES-03-2022-0158
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    Citations

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    Cited by:

    1. Noha Emara & Daniela Zecheru, 2024. "Asymmetric threshold effects of digitization on inflation in emerging markets," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 10(1), pages 1-32, December.
    2. Abdul Karim Kamara, 2024. "The Impact of Financial Inclusion on Economic Growth and Poverty Reduction: Empirical Evidence from sub-Saharan Africa," International Journal of Science and Business, IJSAB International, vol. 32(1), pages 16-33.
    3. Lv, Zhaojiang & Chen, Lan & Ali, Syed Ahtsham & Muda, Iskandar & Alromaihi, Abdullah & Boltayev, Jurabek Yusufovich, 2024. "Financial technologies, green technologies and natural resource nexus with sustainable development goals: Evidence from resource abundant economies using MMQR estimation," Resources Policy, Elsevier, vol. 89(C).
    4. Comlanvi Martin Konou, 2023. "Entrepreneurial Risk and Digital Financial Inclusion: A Cross‐Country Analysis," Economic Papers, The Economic Society of Australia, vol. 42(3), pages 267-281, September.
    5. Lu, Yin & Tian, Tian & Ge, Chen, 2023. "Asymmetric effects of renewable energy, fintech development, natural resources, and environmental regulations on the climate change in the post-covid era," Resources Policy, Elsevier, vol. 85(PB).
    6. Oyebola Etudaiye-Muhtar, Fatima & Johan, Sofia & Lawal, Rodiat & Sakariyahu, Rilwan, 2024. "Fintech, human development and energy poverty in sub-Saharan Africa," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 91(C).
    7. Xia, Aiming & Liu, Qing, 2024. "Modelling the asymmetric impact of fintech, natural resources, and environmental regulations on ecological footprint in G7 countries," Resources Policy, Elsevier, vol. 89(C).
    8. Zhang, Yanhua & Cui, Xiaoyan, 2024. "Fintech, business regulations, and urbanization: Shaping the landscape of natural resource rent in G10 countries," Resources Policy, Elsevier, vol. 89(C).
    9. Dong, Ziguang & Zhou, Zheng & Ananzeh, Mohammed & Hoang, Khai Nguyen & Shamansurova, Zilola & Luong, Tuan Anh, 2024. "Exploring the asymmetric association between fintech, clean energy, climate policy, natural resource conservations and environmental quality. A post-COVID perspective from Asian countries," Resources Policy, Elsevier, vol. 88(C).

    More about this item

    Keywords

    FinTech; Nonlinear; Mobile cellular; Fixed broadband; Internet; Extreme poverty; SSA; C23; G21; O43;
    All these keywords.

    JEL classification:

    • C23 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Models with Panel Data; Spatio-temporal Models
    • G21 - Financial Economics - - Financial Institutions and Services - - - Banks; Other Depository Institutions; Micro Finance Institutions; Mortgages
    • O43 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Institutions and Growth

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